Study of Machine Learning uses Inlithium-Ion Battery Management

Abstract

There is a lot of interest in the important role that machine learning (ML) plays in developing several areas of lithium-ion battery development. This paper looks at the problems, recent developments, and real-world applications of using machine learning to lithium-ion battery research. It highlights the significance of particular machine learning techniques and their revolutionary impacts. Applications of machine learning in the design, manufacturing, maintenance, and end-of-life phases of lithium-ion batteries are covered in the study. Key challenges addressed include limited data availability, complexities in data pre-processing and cleaning, small sample sizes, high computational demands, difficulties in model generalization, lack of transparency in ML models, scalability issues with large datasets, data bias, and the interdisciplinary nature of the field. Proposed solutions include leveraging techniques like Transfer Learning and N-shot Learning to address small dataset limitations, which are highlighted as promising future directions. By presenting these insights, the paper deepens our understanding of ML's role and offers valuable guidance for researchers and practitioners to fully harness its potential.